The neural basis of brain resting state

About This Grant

PROJECT SUMMARY Spontaneous brain activity (i.e., resting state [RS]) is highly structured in both space and time. This salient feature of brain activity is universal across species. Nevertheless, we do not know how or why RS is structured. Despite this major knowledge gap, RS is routinely acquired with functional magnetic resonance imaging (fMRI) and the data is used for insights into brain organization in health and disease. Inferring human brain organization from rs-fMRI is a practice that has grown tremendously over the past 10 years; a trend bound to continue with the proliferation of MR scanners in the United States. But unless we improve our understanding of the neurobiological properties that confer structure onto RS, inferences from RS will not achieve their potential as a window into brain organization. This would jeopardize the impact of hundreds of studies per year and the massive investments from the NIH towards brain mapping (e.g., Human Connectome Project). Here, we propose a series of experiments to close critical knowledge gaps about the neurobiological properties of RS. Our objective is to shed light on the relationship between RS, cortical architecture, and cortical neurophysiology. We use a non- human primate model so that we can deploy high-resolution tools for recording RS, tracing cortical connections, and measuring neurophysiology. We conduct a large portion of our study in motor and somatosensory regions where we can use microelectrode mapping to accurately partition cortex according to function (e.g., hand control vs face control) and cortical area borders (e.g., motor vs premotor cortex). Towards our objective, we propose three Specific Aims. Aim 1 benchmarks cortical parcellation inferred from RS against ground truth cortical divisions. We record RS with fMRI and intrinsic signal optical imaging (rs-ISOI), which is operationally like fMRI but provides higher contrast and spatial resolution. We leverage the statistical dependencies in the recorded time series to generate high resolution maps of cortical networks. The spatial organization of those maps is then quantified by measuring their overlap with anatomical and functional divisions of cortex. Aim 2 benchmarks functional connectivity (rsFC) inferred from rs-ISOI and rs-fMRI against ground truth neuroanatomical connections. rsFC is mapped for sites throughout sensorimotor cortex. We directly compare those connectivity maps to the anatomical maps that we reveal from the same sites using tracers, microstimulation, and fiber tractography. Aim 3 investigates the neurophysiological basis of RS. We place electrode arrays throughout cortical networks and record time series of neurophysiology, rs-ISOI, and rs-fMRI. We then measure the extent of co-fluctuations between the neurophysiology and imaging time series. Our proposed multi-modal approach will shed light on the neurobiology of RS. We will therefore serve vast segments of the neuroscience community that leverage RS. Knowledge gained here will set the stage for next generation connectome projects, which will annotate cortical architecture at the level of cell types, receptors, and genes. Such a resource would transform how our field approaches the functional organization and adaptive rewiring of cortical networks.